Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	20,543
2	New Jersey	19,613
3	Rhode Island	15,991
4	Massachusetts	15,863
5	District of Columbia	14,721
6	Louisiana	13,265
7	Connecticut	13,083
8	Delaware	12,047
9	Arizona	12,020
10	Illinois	11,553
11	Maryland	11,415
12	Nebraska	10,068
13	Mississippi	9,666
14	Iowa	9,590
15	Alabama	8,180
16	Florida	7,873
17	South Dakota	7,791
18	Georgia	7,767
19	South Carolina	7,710
20	Virginia	7,467
21	Utah	7,319
22	Arkansas	7,314
23	Pennsylvania	7,241
24	Michigan	7,194
25	Indiana	7,063
26	Tennessee	6,761
27	Minnesota	6,604
28	North Carolina	6,531
29	Nevada	6,451
30	Texas	6,285
31	California	6,281
32	New Mexico	5,970
33	Colorado	5,788
34	Wisconsin	5,646
35	Kansas	5,226
36	North Dakota	4,810
37	Washington	4,717
38	Ohio	4,633
39	New Hampshire	4,281
40	Missouri	3,761
41	Idaho	3,742
42	Oklahoma	3,674
43	Kentucky	3,664
44	Wyoming	2,678
45	Maine	2,475
46	Puerto Rico	2,382
47	Oregon	2,204
48	Vermont	1,966
49	West Virginia	1,703
50	Alaska	1,673
51	Montana	1,013
52	Hawaii	656

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	590
2	Florida	353
3	South Carolina	327
4	Louisiana	321
5	Texas	270
6	Mississippi	246
7	Georgia	240
8	Tennessee	233
9	Nevada	205
10	California	204
11	Arkansas	200
12	Alabama	199
13	Idaho	171
14	Utah	171
15	North Carolina	153
16	Iowa	146
17	Delaware	121
18	Oklahoma	115
19	Wisconsin	101
20	Ohio	88
21	New Mexico	85
22	Washington	82
23	Minnesota	79
24	Kansas	75
25	Missouri	72
26	Nebraska	70
27	Illinois	66
28	South Dakota	66
29	Maryland	64
30	Alaska	63
31	Oregon	63
32	Virginia	60
33	Indiana	58
34	Wyoming	57
35	Pennsylvania	55
36	Rhode Island	55
37	Kentucky	53
38	Montana	51
39	North Dakota	51
40	Michigan	49
41	Colorado	48
42	District of Columbia	46
43	New Jersey	40
44	Puerto Rico	37
45	West Virginia	34
46	New York	33
47	Maine	27
48	Massachusetts	27
49	Connecticut	26
50	New Hampshire	15
51	Hawaii	10
52	Vermont	10

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,700
2	New York	1,635
3	Connecticut	1,213
4	Massachusetts	1,179
5	Rhode Island	905
6	District of Columbia	784
7	Louisiana	700
8	Michigan	622
9	Illinois	567
10	Maryland	531
11	Pennsylvania	527
12	Delaware	523
13	Indiana	395
14	Mississippi	366
15	Colorado	295
16	New Hampshire	275
17	Minnesota	265
18	Georgia	263
19	Ohio	248
20	Arizona	242
21	New Mexico	239
22	Iowa	227
23	Virginia	212
24	Alabama	200
25	Washington	176
26	Missouri	172
27	Nevada	170
28	Florida	168
29	California	158
30	South Carolina	152
31	Nebraska	146
32	Wisconsin	137
33	Kentucky	135
34	North Carolina	133
35	North Dakota	116
36	South Dakota	109
37	Oklahoma	99
38	Kansas	96
39	Arkansas	92
40	Tennessee	89
41	Vermont	89
42	Texas	88
43	Maine	78
44	Utah	54
45	Idaho	52
46	West Virginia	51
47	Oregon	50
48	Puerto Rico	47
49	Wyoming	34
50	Montana	20
51	Alaska	16
52	Hawaii	12

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	New York	11
2	Arizona	7
3	Louisiana	4
4	New Jersey	4
5	Rhode Island	4
6	South Carolina	4
7	Alabama	3
8	Mississippi	3
9	California	2
10	Florida	2
11	Illinois	2
12	Maryland	2
13	Nevada	2
14	Ohio	2
15	Pennsylvania	2
16	South Dakota	2
17	Virginia	2
18	Arkansas	1
19	Colorado	1
20	Delaware	1
21	District of Columbia	1
22	Georgia	1
23	Indiana	1
24	Kentucky	1
25	Massachusetts	1
26	Michigan	1
27	Minnesota	1
28	Missouri	1
29	Nebraska	1
30	New Hampshire	1
31	New Mexico	1
32	North Carolina	1
33	Tennessee	1
34	Texas	1
35	Alaska	0
36	Connecticut	0
37	Hawaii	0
38	Idaho	0
39	Iowa	0
40	Kansas	0
41	Maine	0
42	Montana	0
43	North Dakota	0
44	Oklahoma	0
45	Oregon	0
46	Puerto Rico	0
47	Utah	0
48	Vermont	0
49	Washington	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	132,045	1	99
Lake	Tennessee	98,347	2	99
Dakota	Nebraska	89,134	3	99
Buena Vista	Iowa	86,901	4	99
Lee	Arkansas	85,243	5	99
Davidson	Tennessee	15,495	177	94
Richland	South Carolina	9,036	469	85
Orange	California	4,932	974	69
York	South Carolina	4,630	1040	66
Pierce	Washington	3,297	1380	56

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,784	1	99
Randolph	Georgia	3,688	2	99
Terrell	Georgia	3,165	3	99
Early	Georgia	3,042	4	99
Emporia city	Virginia	2,619	5	99
Richland	South Carolina	207	672	78
Davidson	Tennessee	166	778	75
Pierce	Washington	115	982	68
Orange	California	111	1004	68
York	South Carolina	43	1558	50

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.